Railway Network across the United States

Weather Data Important Variables

• BEGIN_YEARMONTH: The year and month when the climate event began, formatted as a six-digit number (YYYYMM).

• BEGIN_DAY: The day of the month when the climate event started, represented by a two-digit number.

• END_YEARMONTH: The year and month when the climate event ended, formatted in the same manner

• END_DAY: The day of the month on which the climate event concluded, following the same format

• BEGIN_DAY: The day of the month when the climate event started, represented by a two-digit number.

• EVENT_TYPE: The type of Climate Event being recorded.

• CZ_FIPS: Specific State ID number

• DAMAGE_PROPERTY: The total amount of damage in dollars as a result of an event.

• BEGIN_LAT: The specific Latitude where the event took place.

• BEGIN_LON: The specific Longitude where the event took place.

Rail Data Important Variables

• Objectid: Node id; unique id allocated per node

• Frfranode: an ID for the segment (arc) from which the rail line starts

• Tofranode: an identifier for the node where the rail network line ends

• stateab: The state abbreviation

• county: The Country abbreviation

• division: Section of the US (i.e Mid America)

• timezone: an identifier of timezone

• shape_Length: represents the length of each segment of the rail line

• miles: The length of the segments in miles

Weather Data

Rail Data

Total Damage

Normalized by State Area

The bar graph succinctly prioritizes states based on the financial impact of weather events. Texas emerges as the outlier with the highest damages, suggesting a critical review of its disaster response and infrastructure resilience is warranted. The presence of Midwestern states like Iowa and Nebraska highlights the significant toll of storms in the nation’s agricultural heartland, raising questions about the interplay between climate events and economic vulnerabilities tied to agriculture and land use. Interestingly, coastal and southern states, typically the focus of hurricane-related damages, are interspersed among less frequently discussed states like Illinois and Wisconsin. This distribution prompts us to consider a broader range of climate impacts beyond the obvious high-risk areas. The chart’s data can inform not just reactive policies but proactive investments in technology and infrastructure to fortify states against predictable damages. It also hints at the potential benefits of cross-state learning, where lower-impact states could share best practices in climate event mitigation. This analysis underscores the importance of a strategic, data-informed approach to climate resilience, where the allocation of resources is as dynamic and varied as the weather patterns themselves.

This normalized bar chart provides an adjusted view of the financial impact of weather events, accounting for the size of each state. This normalization allows for a more equitable comparison by illustrating the damage relative to the geographic area, rather than total damages which can be skewed by the size and economic activity of a state. From the chart, Iowa stands out with the highest damage per square mile, indicating that, when size is considered, its relative economic impact from weather events is the most substantial. This could reflect a high density of valuable assets or infrastructure within a smaller area, or an exceptional severity of weather events. States like Ohio, Mississippi, and New Jersey follow, suggesting these states, though varying in size and geography, face significant impacts from weather events relative to their area. Interestingly, larger states with significant total damages like Texas appear further down the list when the damage is adjusted per square mile, highlighting the importance of considering geographic scale in such analyses. The chart also informs us that less geographically extensive states with high population densities or substantial infrastructure—like Delaware and New Jersey—may experience high normalized damages, underscoring the potential for significant impact in smaller areas.

Our dataset for the rail component consists of rail lines spanning all 50 states, Mexico, Canada, and DC. It contains information such as timezone, length of segments in miles, to and from node values, etc. We did not subset this data, however, we did narrow the scope to Continental US states only.

Network Analysis is a method used to find the shortest path using a specific network. It applies to a multitude of applications. i.e. planes, cars, social media analytics, etc.​ It quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. While there are many different methods of conducting network analysis, betweenness was found to be the best for this project.

Top Centrality Node in the US

Top Five Centrality Node in the US

After calculating the betweenness centrality for the entire continental US, we found that the node with the highest value, 0.22, is located in Lima, Ohio. This is significant because the Lima railroad station served as a major hub, connecting five major continental railroads across the US in the early twentieth century. These railroads included the Pennsylvania Railroad, Baltimore and Ohio Railroad, New York Railroad, Chicago and St. Louis Railroad, Erie Railroad, and Detroit, Toledo, and Ironton Railroad. By the 1990s, all passenger rail lines had been discontinued. Currently, it has been restored and serves as both a museum and an office.

Based on the visualization, we can see that the top 5 nodes across the US are located in the same general area, although we can only see 3 points, this just means that the nodes are closely located to one another. We can further see a breakdown in location in the chart below.

  Nodeid Centralityscore Latitude Longitude                        Location
1 435096          0.2258 40.74490  -84.1042                 Lima, OH, 45801
2 435236          0.2236 40.74570  -84.0882                 Lima, OH, 45804
3 416842          0.2225 41.56690  -87.4178            Calumet Township, IN
4 429164          0.2223 41.07137  -85.1283 Hanna Creighton, Fort Wayne, IN
5 429161          0.2221 41.07140  -85.1302           LaRez, Fort Wayne, IN

Rail Line Density across the United States

Rail Line Density Normalized Per State Area Across the United States

The purpose of this side-by-side comparison of heatmaps is to show the difference between the density of rail lines and the railway mileage of each state. The heatmap to the left shows the total railway miles per US state. From our US DOT rail data, we extracted the state column and miles column which states the distance of individual railway segments that are recorded. The total railway miles for a state were computed by summing up all of the individual railway segments. We can see that Texas has the most railway mileage, followed by Illinois and California. For Texas and California, their large square mileage and high number of export products are leading factors in the ability for more rail lines to run along these states. Illinois’s high concentration of rail lines can be explained by numerous factors such as the state being the center of many of the nation’s rail networks and Chicago being the largest US rail gateway.

The heatmap to the right shows the level of railway densities per state by using the summation of the total railway miles and dividing it by the area of a state in square miles. You can tell in states like California and Texas that had high railway mileage, the railway density is on the lower end. Some states such as Florida, Virginia, and North Carolina seem to have stayed in a relatively similar range between the railway mileage and railway density comparisons. There is a pattern of Northeastern states like Illinois, Ohio, and Pennsylvania that have high railway densities and can be historically attributed to being important hubs for the transportation of various goods and a vital junction of rail lines that run from the East to the West. We can see that New Jersey is the most densely populated in railways due to being the most densely populated state and being neighboring states to major cities like Philadelphia and New York City. The state is also home to very large economic activity and a high volume of freight rail traffic.